Master of Science in Computer Information Systems

Permanent URI for this collectionhttp://41.89.31.6:4000/handle/123456789/74

Welcome to Master of Science in Computer Information Systems collection

Browse

Now showing 1 - 2 of 2

Framework for Adoption of Cloud Computing by Small and Medium-Sized Enterprises in Meru County.
(KeMU, 2021-03) Odero, Eunice Achieng
Cloud Computing is a technology that has emerged in the market and it is widely used across the world for data processing of information and storage capability. It is a growing trend and is seen as a game-changer to SMEs business growth and development as far as technology deployment and usage is concerned. Organizations everywhere are trying to leverage Cloud Computing to achieve business missions and goals. Many believe it's just a marketing strategy while others see it as a benefit on how information technology is delivered. In the case of Kenya, many SMEs are still struggling to survive in an on-going global business cycle for growth and development. Cloud Computing on the other hand offers many opportunities that can help such SMEs improve their business and use technology more efficiently and effectively to reduce the cost of equipment and services. The main objective of the study was to develop a framework for the adoption of Cloud Computing by SMEs, covering: identification of usage, challenges and decision making support in the process of Cloud Computing adoption. The usage of Cloud Computing is still low among SMEs in Meru County mainly because of lack of expertise to give them training. Moreover, Cloud Computing adoption is impeded by the competing interest to deal with challenges such as poor network connectivity, need to ensure ease of use, ensure manageable cost implications and competition. The study developed a framework for adoption of Cloud Computing to be used by SMEs in Meru County. The theoretical underpinning of the framework is based on the theories of reasoned action, technology acceptance model and interplay of technology, organization and environment among others. A descriptive research design was adopted in conducting this study and stratified sampling was done covering the various players in the adoption process who included ICT managers, business owners and consultants. The data analysis was both qualitative and quantitative, the data analysis tools used included a laptop and SPSS software. A survey was carried on SMEs within Meru County. Primary data was analyzed to show the relationship between independent and dependent variables. This study ensured that all ethical considerations such as confidentiality, privacy and integrity were observed. The results in this study shows that decision making, application used and the type of model used are the major factors to be considered for adoption. A framework was proposed that SMEs can adopt and reap the benefits of a systematic adoption of Cloud Computing. This framework is from the results of this study and it has been demonstrated that SMEs and other stakeholders will achieve cost cutting, reduced timeframes in embracing technology and enhanced fit in the technology ecosystem that the SMEs operate in. The study focused on storage of data.
A Framework for Optimizing Pharmacy Inventory Management System Performance Using Cloud Computing and Machine Learning a Case Study of Nairobi County
(KeMU, 2024-09) Chebet, Kelvin
The purpose of this study was to address how pharmacy inventory management systems can be improved using cloud computing and machine learning. The main aim was to enhance efficacy, accuracy and efficiency in inventory management practices within the pharmaceutical sector. The problem identified was about the inefficiencies and challenges present in conventional stock control methods such as manual tracking mechanisms and outdated ones. Because of these inefficiencies, issues such as stock-outs, excesses, and lack of real-time information critical for decision-making processes arise. To overcome this challenge, a quantitative research design was used where data was collected through questionnaires and interviews from a diverse group of pharmacy personnel. The sample included public and private pharmacies in Nairobi County through stratified random sampling. The methodology involves the use of questionnaires for quantitative data collection on ongoing inventory management practices as well as technological readiness. This study expects that by utilizing cloud computing and machine learning algorithms there will be an inclusive framework created for optimizing pharmacy inventory management systems. The results indicated a need for the implementation of the proposed machine learning and cloud computing framework as the respondent indicated a high dissatisfaction it their current inventory management systems which were indicated to have major challenges that contributed to financial losses, customer dissatisfaction among other. Additionally, this research provides practical recommendations for implementing cloud computing platforms or machine learning solutions which could transform the traditional approach to inventory management thereby enhancing patient care outcomes.